using Gadfly, RDatasets
set_default_plot_size(21cm, 8cm)
p1 = plot(dataset("datasets", "iris"), x="SepalLength", y="SepalWidth",
Geom.point)
p2 = plot(dataset("datasets", "iris"), x="SepalLength", y="SepalWidth",
Stat.binmean, Geom.point)
hstack(p1,p2)
SepalLength
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
0
5
10
15
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
10.2
10.4
10.6
10.8
11.0
11.2
11.4
11.6
11.8
12.0
7.6253.0875
7.1400000000000013.2
6.8571428571428563.0714285714285716
6.68000000000000153.0300000000000002
6.5,3.0
6.39999999999999952.9571428571428577
6.2999999999999992.8555555555555556
6.22.8249999999999997
6.052.7916666666666665
5.9000000000000013.0666666666666664
5.7999999999999992.8857142857142857
5.7000000000000013.1
5.60000000000000052.816666666666667
5.4428571428571433.207142857142857
5.2,3.425
5.10000000000000053.477777777777778
4.96253.05625
4.7714285714285713.185714285714286
4.4888888888888893.0777777777777775
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
3.75
4.00
4.25
4.50
2.00
2.05
2.10
2.15
2.20
2.25
2.30
2.35
2.40
2.45
2.50
2.55
2.60
2.65
2.70
2.75
2.80
2.85
2.90
2.95
3.00
3.05
3.10
3.15
3.20
3.25
3.30
3.35
3.40
3.45
3.50
3.55
3.60
3.65
3.70
3.75
3.80
3.85
3.90
3.95
4.00
4.05
4.10
4.15
4.20
4.25
2
3
4
5
2.00
2.05
2.10
2.15
2.20
2.25
2.30
2.35
2.40
2.45
2.50
2.55
2.60
2.65
2.70
2.75
2.80
2.85
2.90
2.95
3.00
3.05
3.10
3.15
3.20
3.25
3.30
3.35
3.40
3.45
3.50
3.55
3.60
3.65
3.70
3.75
3.80
3.85
3.90
3.95
4.00
4.05
4.10
4.15
4.20
4.25
SepalWidth
SepalLength
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
0
5
10
15
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
10.2
10.4
10.6
10.8
11.0
11.2
11.4
11.6
11.8
12.0
5.9,3.0
6.2,3.4
6.5,3.0
6.3,2.5
6.7,3.0
6.7,3.3
6.8,3.2
5.8,2.7
6.9,3.1
6.7,3.1
6.9,3.1
6.0,3.0
6.4,3.1
6.3,3.4
7.7,3.0
6.1,2.6
6.3,2.8
6.4,2.8
7.9,3.8
7.4,2.8
7.2,3.0
6.4,2.8
6.1,3.0
6.2,2.8
7.2,3.2
6.7,3.3
6.3,2.7
7.7,2.8
5.6,2.8
6.9,3.2
6.0,2.2
7.7,2.6
7.7,3.8
6.5,3.0
6.4,3.2
5.8,2.8
5.7,2.5
6.8,3.0
6.4,2.7
6.5,3.2
7.2,3.6
6.7,2.5
7.3,2.9
4.9,2.5
7.6,3.0
6.5,3.0
6.3,2.9
7.1,3.0
5.8,2.7
6.3,3.3
5.7,2.8
5.1,2.5
6.2,2.9
5.7,2.9
5.7,3.0
5.6,2.7
5.0,2.3
5.8,2.6
6.1,3.0
5.5,2.6
5.5,2.5
5.6,3.0
6.3,2.3
6.7,3.1
6.0,3.4
5.4,3.0
6.0,2.7
5.8,2.7
5.5,2.4
5.5,2.4
5.7,2.6
6.0,2.9
6.7,3.0
6.8,2.8
6.6,3.0
6.4,2.9
6.1,2.8
6.3,2.5
6.1,2.8
5.9,3.2
5.6,2.5
6.2,2.2
5.8,2.7
5.6,3.0
6.7,3.1
5.6,2.9
6.1,2.9
6.0,2.2
5.9,3.0
5.0,2.0
5.2,2.7
6.6,2.9
4.9,2.4
6.3,3.3
5.7,2.8
6.5,2.8
5.5,2.3
6.9,3.1
6.4,3.2
7.0,3.2
5.0,3.3
5.3,3.7
4.6,3.2
5.1,3.8
4.8,3.0
5.1,3.8
5.0,3.5
4.4,3.2
4.5,2.3
5.0,3.5
5.1,3.4
4.4,3.0
4.9,3.6
5.5,3.5
5.0,3.2
4.9,3.1
5.5,4.2
5.2,4.1
5.4,3.4
4.8,3.1
4.7,3.2
5.2,3.4
5.2,3.5
5.0,3.4
5.0,3.0
4.8,3.4
5.1,3.3
4.6,3.6
5.1,3.7
5.4,3.4
5.1,3.8
5.7,3.8
5.1,3.5
5.4,3.9
5.7,4.4
5.8,4.0
4.3,3.0
4.8,3.0
4.8,3.4
5.4,3.7
4.9,3.1
4.4,2.9
5.0,3.4
4.6,3.4
5.4,3.9
5.0,3.6
4.6,3.1
4.7,3.2
4.9,3.0
5.1,3.5
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
-2.5
0.0
2.5
5.0
7.5
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.0
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4.0
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
5.0
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
6.0
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
7.0
SepalWidth
using DataFrames, Gadfly, Distributions
set_default_plot_size(21cm, 8cm)
x = -4:0.1:4
Da = [DataFrame(x=x, ymax=pdf.(Normal(μ),x), ymin=0.0, u="μ=$μ") for μ in [-1,1]]
Db = [DataFrame(x=randn(200).+μ, u="μ=$μ") for μ in [-1,1]]
p1 = plot(vcat(Da...), x=:x, y=:ymax, ymin=:ymin, ymax=:ymax, color=:u,
Geom.line, Geom.ribbon, Guide.ylabel("Density"), Theme(alphas=[0.6]),
Guide.colorkey(title="", pos=[2.5,0.6]), Guide.title("Parametric PDF")
)
p2 = plot(vcat(Db...), x=:x, color=:u, Theme(alphas=[0.6]),
Stat.density(bandwidth=0.5), Geom.polygon(fill=true, preserve_order=true),
Coord.cartesian(xmin=-4, xmax=4, ymin=0, ymax=0.4),
Guide.colorkey(title="", pos=[2.5,0.6]), Guide.title("Kernel PDF")
)
hstack(p1,p2)
x
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
-12.0
-11.5
-11.0
-10.5
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
-20
-10
0
10
20
-12.0
-11.5
-11.0
-10.5
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
μ=-1
μ=1
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
-0.5
0.0
0.5
1.0
-0.40
-0.38
-0.36
-0.34
-0.32
-0.30
-0.28
-0.26
-0.24
-0.22
-0.20
-0.18
-0.16
-0.14
-0.12
-0.10
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
0.52
0.54
0.56
0.58
0.60
0.62
0.64
0.66
0.68
0.70
0.72
0.74
0.76
0.78
0.80
0.82
Kernel PDF
x
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
-12.0
-11.5
-11.0
-10.5
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
-20
-10
0
10
20
-12.0
-11.5
-11.0
-10.5
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
μ=-1
μ=1
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
-0.5
0.0
0.5
1.0
-0.40
-0.38
-0.36
-0.34
-0.32
-0.30
-0.28
-0.26
-0.24
-0.22
-0.20
-0.18
-0.16
-0.14
-0.12
-0.10
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
0.52
0.54
0.56
0.58
0.60
0.62
0.64
0.66
0.68
0.70
0.72
0.74
0.76
0.78
0.80
0.82
Density
Parametric PDF
using CategoricalArrays
using Gadfly
set_default_plot_size(14cm, 8cm)
n = 400
group = repeat([-1, 1], inner=200)
x = randn(n) .+ group
plot(x=x, color=categorical(group), Guide.colorkey(title="", pos=[3.6,0.7]),
layer(Stat.density, Geom.line, Geom.polygon(fill=true, preserve_order=true), alpha=[0.4]),
layer(Stat.quantile_bars(quantiles=[0.05, 0.95]), Geom.segment),
Guide.title("Density with bars showing the central 90% CI"),
Guide.ylabel("Density"), Coord.cartesian(xmin=-4, xmax=4)
)
x
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
-12.0
-11.5
-11.0
-10.5
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
-20
-10
0
10
20
-12.0
-11.5
-11.0
-10.5
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-1
1
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
-0.50
-0.45
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
-0.5
0.0
0.5
1.0
-0.50
-0.48
-0.46
-0.44
-0.42
-0.40
-0.38
-0.36
-0.34
-0.32
-0.30
-0.28
-0.26
-0.24
-0.22
-0.20
-0.18
-0.16
-0.14
-0.12
-0.10
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
0.52
0.54
0.56
0.58
0.60
0.62
0.64
0.66
0.68
0.70
0.72
0.74
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0.86
0.88
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0.98
1.00
Density
Density with bars showing the central 90% CI
using DataFrames, Gadfly, RDatasets, Statistics
set_default_plot_size(21cm, 8cm)
salaries = dataset("car","Salaries")
salaries.Salary /= 1000.0
salaries.Discipline = ["Discipline $(x)" for x in salaries.Discipline]
df = combine(groupby(salaries, [:Rank, :Discipline]), :Salary.=>mean)
df.label = string.(round.(Int, df.Salary_mean))
p1 = plot(df, x=:Discipline, y=:Salary_mean, color=:Rank,
Scale.x_discrete(levels=["Discipline A", "Discipline B"]),
label=:label, Geom.label(position=:centered), Stat.dodge(position=:stack),
Geom.bar(position=:stack)
)
p2 = plot(df, y=:Discipline, x=:Salary_mean, color=:Rank,
Coord.cartesian(yflip=true), Scale.y_discrete,
label=:label, Geom.label(position=:right), Stat.dodge(axis=:y),
Geom.bar(position=:dodge, orientation=:horizontal),
Scale.color_discrete(levels=["Prof", "AssocProf", "AsstProf"]),
Guide.yticks(orientation=:vertical), Guide.ylabel(nothing)
)
hstack(p1, p2)
Salary_mean
-200
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-95
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Prof
AssocProf
AsstProf
Rank
133
85
101
120
83
74
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
Discipline B
Discipline A
Discipline B
Discipline A
Discipline
Discipline A
Discipline B
Prof
AsstProf
AssocProf
Rank
133
85
101
120
83
74
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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Salary_mean
using DataFrames, Gadfly
set_default_plot_size(14cm, 8cm)
sigmoid(x) = 1 ./ (1 .+ exp.(-x))
npoints = 30
gshift, x = rand([0,2], npoints), range(-9, 9, length=npoints)
y, ye = sigmoid(x+gshift), 0.2*rand(npoints)
df = DataFrame(x=x, y=y, ymin=y-ye, ymax=y+ye, g=gshift)
plot(y=[sigmoid, x->sigmoid(x+2)], xmin=[-10], xmax=[10],
Geom.line, Stat.func(100), color=[0,2], Guide.xlabel("x"),
layer(df, x=:x, y=:y, ymin=:ymin, ymax=:ymax, color=:g,
Geom.point, Geom.yerrorbar, Stat.x_jitter(range=1)),
Scale.color_discrete_manual("deepskyblue","yellow3", levels=[0,2]),
Guide.colorkey(title="Function", labels=["Sigmoid(x)", "Sigmoid(x+2)"]),
Theme(errorbar_cap_length=0mm, key_position=:inside)
)
x
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9.0141046600311480.9998766054240137
8.6927540025953610.9999689322859989
8.1439459673205050.9995731370872474
7.5339262418893150.9992062365897446
6.6189131357520840.9998000496254946
5.547565393392760.9996281141773367
5.0757871084144220.999308435318234
4.3911606127179690.9987143089615129
4.1168057252619660.982612832728348
3.1354122445179220.9681328340340032
2.73212982377129960.9423020076914295
2.3910512889365950.8977447634316474
1.2028286926363470.9721241862548583
0.76812760129307690.9493594320914616
0.151905903768971230.5769694274020658
-0.249320961278248680.8441788063113398
-0.8281913982033450.7444001360376136
-1.58342552357296950.610229226597668
-2.10681175390071160.45700301154629586
-3.18894341318910430.05769799230857057
-3.2464350186055960.03186716596599677
-4.1491536301362420.017387167271651932
-4.6106589343894060.06567092533092088
-4.9267611518636330.036408609346018014
-5.8616504657392570.002741371708568133
-6.7756507565596650.010801164785603749
-7.01153531072300850.00583556787482027
-7.9328016384674130.00042686291275275794
-8.4381743082221340.00022951552431103904
-8.9822199994533580.0009110511944006454
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
Sigmoid(x)
Sigmoid(x+2)
Function
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y
using Distributions, Gadfly, RDatasets
set_default_plot_size(21cm, 8cm)
iris, geyser = dataset.("datasets", ["iris", "faithful"])
df = combine(groupby(iris, :Species), :SepalLength=>(x->fit(Normal, x))=>:d)
ds2 = fit.([Normal, Uniform], [geyser.Eruptions])
yeqx(x=4:6) = layer(x=x, Geom.abline(color="gray80"))
xylabs = [Guide.xlabel("Theoretical q"), Guide.ylabel("Sample q")]
p1 = plot(df, x=:d, y=iris[:,1], color=:Species, Stat.qq, yeqx(4:8),
xylabs..., Guide.title("3 Samples, 1 Distribution"))
p2 = plot(geyser, x=ds2, y=:Eruptions, color=["Normal","Uniform"], Stat.qq,
yeqx(0:6), xylabs..., Guide.title("1 Sample, 2 Distributions"),
Theme(discrete_highlight_color=c->nothing, alphas=[0.5], point_size=2pt)
)
hstack(p1, p2)
Theoretical q
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-7.5
-7.0
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-10
0
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20
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-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
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0.0
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15.5
16.0
Normal
Uniform
Color
5.0870848708487085.067
5.0741697416974175.033
5.0612546125461265.0
5.0483394833948344.933
5.0354243542435424.933
5.022509225092254.933
5.0095940959409594.9
4.9966789667896684.8999999999999995
4.9837638376383764.883
4.9708487084870844.85
4.9579335793357924.833
4.9450184501845024.833
4.932103321033214.817
4.91918819188191854.817
4.9062730627306274.8
4.8933579335793364.8
4.8804428044280444.8
4.8675276752767534.8
4.8546125461254614.8
4.8416974169741694.8
4.8287822878228784.783
4.8158671586715874.767
4.8029520295202954.75
4.7900369003690034.732999999999999
4.7771217712177124.716000000000001
4.764206642066424.7
4.7512915129151294.7
4.7383763837638374.7
4.7254612546125454.7
4.71254612546125354.7
4.6996309963099634.7
4.68671586715867154.667
4.673800738007384.666999999999999
4.6608856088560894.650000000000001
4.6479704797047974.633
4.6350553505535064.633
4.6221402214022144.633
4.6092250922509224.617
4.596309963099634.6
4.5833948339483394.6
4.5704797047970484.6
4.5575645756457564.6
4.5446494464944644.583
4.5317343173431734.583
4.5188191881918824.583
4.505904059040594.583
4.4929889298892984.567
4.48007380073800654.567
4.46715867158671554.567
4.45424354243542454.549999999999999
4.4413284132841334.533
4.4284132841328414.533
4.4154981549815494.533
4.4025830258302594.533
4.3896678966789674.533
4.3767527675276754.517
4.3638376383763834.5
4.3509225092250924.5
4.33800738007384.5
4.3250922509225094.5
4.3121771217712174.5
4.2992619926199254.5
4.2863468634686344.5
4.2734317343173434.5
4.26051660516605154.4830000000000005
4.247601476014764.467
4.2346863468634694.467
4.2217712177121774.45
4.2088560885608864.45
4.1959409594095944.449999999999999
4.1830258302583024.433
4.1701107011070114.433
4.157195571955724.417
4.1442804428044284.417
4.1313653136531364.417
4.1184501845018454.417
4.1055350553505534.4
4.0926199261992624.383
4.079704797047974.367
4.0667896678966784.367
4.0538745387453874.367
4.04095940959409554.365999999999999
4.02804428044280454.3500000000000005
4.0151291512915134.35
4.0022140221402214.35
3.98929889298892974.35
3.97638376383763834.333
3.96346863468634644.333
3.95055350553505544.333
3.93763837638376354.333
3.9247232472324724.333
3.91180811808118064.317000000000001
3.8988929889298894.3
3.88597785977859774.3
3.87306273062730584.283
3.86014760147601484.283
3.8472324723247234.267
3.83431734317343144.267
3.821402214022144.25
3.80848708487084854.25
3.7955719557195574.25
3.78265682656826564.25
3.7697416974169744.2330000000000005
3.75682656826568234.233
3.74391143911439134.233
3.73099630996309944.2
3.7180811808118084.183
3.70516605166051654.167
3.6922509225092254.167
3.6793357933579334.167
3.66642066420664174.166999999999999
3.65350553505535074.150000000000001
3.6405904059040594.15
3.62767527675276744.15
3.6147601476014764.15
3.60184501845018454.133
3.58892988929889264.133
3.57601476014760164.117
3.56309963099630974.116999999999999
3.5501845018450184.1
3.53726937269372684.1
3.52435424354243534.083
3.5114391143911444.083
3.4985239852398524.083
3.4856088560885614.083
3.4726937269372694.083
3.4597785977859784.067
3.4468634686346864.066999999999999
3.43394833948339474.05
3.42103321033210334.033
3.40811808118081144.033
3.39520295202952044.000000000000001
3.38228782287822854.0
3.36937269372693754.0
3.35645756457564564.0
3.3435424354243544.0
3.33062730627306274.0
3.31771217712177123.967
3.304797047970483.966
3.2918819188191883.95
3.27896678966789653.95
3.2660516605166053.917
3.25313653136531363.917
3.2402214022140223.917
3.227306273062733.883
3.2143911439114393.85
3.20147601476014733.85
3.18856088560885633.8330000000000006
3.17564575645756443.833
3.1627306273062733.833
3.14981549815498153.833
3.13690036900368963.8329999999999997
3.12398523985239863.817
3.11107011070110673.767
3.09815498154981533.767
3.0852398523985243.75
3.07232472324723243.733
3.0594095940959413.7170000000000005
3.04649446494464953.683
3.0335793357933583.6
3.0206642066420663.6
3.00774907749077473.6
2.99483394833948323.6
2.9819188191881923.567
2.96900369003690033.5669999999999993
2.9560885608856093.5
2.94317343173431743.5
2.93025830258302563.450000000000001
2.91734317343173453.417
2.90442804428044273.367
2.8915129151291513.333
2.87859778597785983.333
2.8656826568265683.317
2.8527675276752773.067
2.8398523985239852.8999999999999995
2.8269372693726942.883
2.8140221402214022.800000000000001
2.80110701107011062.6330000000000022
2.7881918819188192.617
2.77527675276752772.483
2.76236162361623632.417
2.74944649446494442.417
2.7365313653136532.4
2.72361623616236152.4
2.710701107011072.3829999999999996
2.69778597785977862.367
2.6848708487084872.3500000000000005
2.67195571955719572.333
2.6590405904059042.317
2.6461254612546132.3
2.6332103321033212.2829999999999995
2.62029520295202942.267
2.6073800738007382.25
2.59446494464944652.25
2.5815498154981552.233
2.56863468634686362.233
2.5557195571955722.2170000000000005
2.54280442804428032.2
2.5298892988929892.2
2.51697416974169742.2
2.5040590405904062.183
2.49114391143911452.167
2.47822878228782262.167
2.46531365313653162.15
2.45239852398523972.133
2.43948339483394832.1
2.4265682656826572.1
2.41365313653136542.1
2.4007380073800742.083
2.3878228782287822.083
2.3749077490774912.067
2.3619926199261992.033
2.34907749077490772.033
2.33616236162361622.017
2.3232472324723252.017
2.31033210332103332.017
2.29741697416974142.0
2.284501845018452.0
2.27158671586715852.0
2.2586715867158672.0
2.24575645756457561.983
2.2328413284132841.983
2.21992619926199271.9829999999999999
2.20701107011070131.9670000000000003
2.194095940959411.967
2.1811808118081181.967
2.16826568265682651.95
2.1553505535055351.933
2.14243542435424361.933
2.1295202952029521.917
2.11660516605166031.9169999999999998
2.10369003690036931.883
2.09077490774907741.883
2.0778597785977861.883
2.06494464944649451.883
2.0520295202952031.867
2.03911439114391161.867
2.026199261992621.867
2.01328413284132871.867
2.0003690036900371.867
1.98745387453874531.867
1.97453874538745391.867
1.96162361623616241.867
1.9487084870848711.85
1.93579335793357931.85
1.92287822878228791.833
1.90996309963099641.833
1.89704797047970471.833
1.88413284132841331.833
1.87121771217712191.833
1.85830258302583041.833
1.84538745387453871.833
1.83247232472324731.817
1.81955719557195581.817
1.80664206642066421.817
1.79372693726937271.8
1.78081180811808131.8
1.76789667896678981.8
1.75498154981549811.8
1.74206642066420671.783
1.72915129151291521.783
1.71623616236162381.75
1.70332103321033211.75
1.69040590405904071.75
1.67749077490774921.75
1.66457564575645761.75
1.6516605166051661.75
1.63874538745387471.733
1.62583025830258321.7
1.61291512915129151.667
6.5401059126598185.067
6.2655669112023745.033
6.0943827937547115.0
5.96737024884597664.933
5.86528793030014.933
5.7793432161796754.933
5.7047500751407644.9
5.6386029069723414.8999999999999995
5.5789998638629124.883
5.5246254048191634.85
5.47453017303263154.833
5.4280056880042284.833
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h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
-10
0
10
20
-6.0
-5.8
-5.6
-5.4
-5.2
-5.0
-4.8
-4.6
-4.4
-4.2
-4.0
-3.8
-3.6
-3.4
-3.2
-3.0
-2.8
-2.6
-2.4
-2.2
-2.0
-1.8
-1.6
-1.4
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
10.2
10.4
10.6
10.8
11.0
11.2
11.4
11.6
11.8
12.0
Sample q
1 Sample, 2 Distributions
Theoretical q
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
-5
0
5
10
15
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
10.2
10.4
10.6
10.8
11.0
11.2
11.4
11.6
11.8
12.0
12.2
12.4
12.6
12.8
13.0
13.2
13.4
13.6
13.8
14.0
setosa
versicolor
virginica
Species
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5.6709946027415255.300000000000001
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5.5777338944459845.1
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5.5554053942882325.1
5.5439653161872135.1
5.53232526990185755.099999999999999
5.5204718775110255.0
5.5083905469787365.0
5.4960653117674175.0
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5.3376536839710894.9
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5.2421376632141024.8
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5.1432593693421094.7
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5.080169266044534.6
5.0430160782498014.6
5.0004358445754854.6
4.9501500952079734.5
4.8879810380321324.4
4.8048080222899354.4
4.672670490656414.4
5.8687189375638397.7
5.7784831360092527.7
5.7216848618896417.7
5.6792300450272117.7
5.644890256438347.6
5.61581251093723657.399999999999998
5.5904408573311777.3
5.5678291161789557.2
5.54735706447656357.2
5.5285951143107347.2
5.5112328741326497.1
5.4950384390105617.0
5.4798337651263466.9
5.4654790412517616.9
5.4518623705869326.9
5.43889272935906656.9
5.4264950243766656.8
5.4146065387073186.8
5.4031743210661276.8
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5.3815044602197876.7
5.3711943713513326.7
5.36119361525526.7
5.351476413875336.7
5.3420199936365536.7
5.3328041264765786.7
5.3238107556463496.7
5.3150236880838066.600000000000001
5.3064283395760276.599999999999998
5.2980115221518396.5
5.2897612655370086.5
5.2816666662940446.5
5.2737177596230416.5
5.2659054098346756.5
5.2582212163041286.4
5.25065743233472756.4
5.2432068948458396.4
5.2358629631829986.4
5.2286194656529686.4
5.2214706526301826.4
5.214411155277276.399999999999999
5.2074359490812256.3
5.2005403215361146.3
5.1937198434091076.3
5.18697034311365456.3
5.1802878837855866.3
5.1736687427176526.3
5.1671093928577966.3
5.1606064861181336.3
5.1541568382766286.3
5.1477574152829926.2
5.1414053208053176.2
5.1350977848751746.2
5.12883215350698856.199999999999998
5.1226058791829526.1
5.1164165121079136.1
5.1102616921500986.1
5.1041391413932576.1
5.0980466572342616.1
5.0919821059674816.1
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5.0560691519006546.0
5.0501450077206585.9
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5.01480644196837755.8
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4.9971935580316215.8
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4.979558195275775.7
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4.961854992279345.7
4.9559308480993455.7
4.9499922364203085.7
4.9440373573090295.6
4.9380643770086055.6
4.9320714237242535.6
4.9260565831964275.6
4.9200178940325185.6
4.9139533427657385.6
4.9078608586067425.5
4.9017383078499015.5
4.8955834878920865.5
4.8893941208170465.5
4.883167846493015.5
4.8769022151248255.5
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4.8642425847170065.4
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4.8383312572823475.4
4.8317121162144135.4
4.8250296568863445.300000000000001
4.8182801565908925.2
4.8114596784638845.2
4.8045640509187735.2
4.7975888447227285.2
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4.7833805343470315.1
4.7761370368175.1
4.768793105154165.1
4.7613425676652715.1
4.75377878369587055.1
4.7460945901653245.1
4.7382822403769575.1
4.7303333337059545.099999999999999
4.7222387344629915.0
4.713988477848165.0
4.70557166042397155.0
4.6969763119161935.0
4.688189244353655.0
4.6791958735234215.0
4.6699800063634465.0
4.6605235861246685.0
4.6508063847447995.0
4.6408056286486675.0
4.63049553978021144.9
4.6198467675656574.9
4.6088256789338714.9
4.5973934612926814.9
4.58550497562333354.9
4.5731072706409324.9
4.5601376294130674.8
4.5465209587482374.8
4.5321662348736534.8
4.5169615609894374.8
4.500767125867354.8
4.4834048856892644.7
4.4646429355234354.7
4.4441708838210444.6
4.4215591426688224.6
4.3961874890627624.6
4.3671097435616594.6
4.3327699549727884.5
4.2903151381103574.4
4.2335168639907474.4
4.1432810624361594.4
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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12.0
Sample q
3 Samples, 1 Distribution
using Compose, Gadfly, RDatasets
set_default_plot_size(21cm,8cm)
salaries = dataset("car","Salaries")
salaries.Salary /= 1000.0
salaries.Discipline = ["Discipline $(x)" for x in salaries.Discipline]
p = plot(salaries[salaries.Rank.=="Prof",:], x=:YrsService, y=:Salary,
color=:Sex, xgroup = :Discipline,
Geom.subplot_grid(Geom.point,
layer(Stat.smooth(method=:lm, levels=[0.95, 0.99]), Geom.line, Geom.ribbon)),
Scale.xgroup(levels=["Discipline A", "Discipline B"]),
Guide.colorkey(title="", pos=[0.43w, -0.4h]),
Theme(point_size=2pt, alphas=[0.5])
)
YrsService by
Discipline B
Discipline A
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21,145.028
20,138.0
27,142.5
38,93.519
49,186.96
28,144.309
33,128.25
27,142.023
10,107.986
35,150.376
31,162.15
38,114.596
17,124.312
11,106.231
15,137.317
25,128.464
12,145.0
23,98.053
38,151.445
19,145.098
10,105.45
9,116.518
60,192.253
23,134.778
37,151.65
15,124.714
31,162.221
15,161.101
23,104.428
15,135.027
16,134.55
45,67.559
20,129.6
10,145.2
21,170.0
11,119.5
11,146.0
11,145.35
19,126.2
7,128.4
39,111.35
20,163.2
18,120.0
33,162.2
2,96.545
5,165.0
17,152.5
17,160.4
40,119.7
22,114.5
33,189.409
18,122.1
22,133.7
9,180.0
19,153.75
10,107.5
30,134.0
23,101.0
22,150.0
5,141.136
11,142.467
14,147.349
25,111.751
20,134.185
24,93.164
19,151.575
18,181.257
19,130.664
16,167.284
8,105.89
19,176.5
16,137.167
21,118.971
9,111.168
12,128.148
26,144.651
27,156.938
28,119.015
27,112.696
14,127.512
5,153.303
23,126.933
25,133.217
26,106.689
14,102.235
7,129.676
20,123.683
38,166.024
25,172.272
37,152.708
14,132.825
18,122.96
20,144.64
16,135.585
28,150.743
19,193.0
3,150.48
23,113.398
34,92.391
19,100.131
45,146.856
2,126.32
36,91.412
17,111.512
31,99.418
12,101.0
31,109.785
21,117.704
9,106.639
11,108.875
28,126.621
25,140.096
19,151.768
28,98.193
15,114.778
19,94.384
38,231.545
27,101.299
2,146.5
31,125.196
21,155.75
9,117.256
4,132.261
8,118.223
20,101.0
3,117.15
18,104.8
18,129.0
20,119.25
45,147.765
23,175.0
41,141.5
39,115.0
16,173.2
18,139.75
15,95.329
25,101.738
19,150.564
30,103.106
19,151.292
19,166.605
18,186.023
36,119.45
15,109.305
27,139.219
9,114.33
21,125.192
44,105.0
23,172.505
38,150.68
26,103.649
19,103.275
26,136.66
7,109.707
20,110.515
31,134.69
30,131.95
10,115.435
40,101.036
43,205.5
30,138.771
15,109.646
11,121.946
14,109.954
35,107.309
40,88.709
6,146.8
35,100.351
19,94.35
9,108.1
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15,166.8
28,122.5
31,111.35
44,144.05
4,105.26
13,170.5
16,127.1
36,88.6
43,72.3
11,148.8
18,126.3
36,97.15
7,107.3
9,183.8
28,168.5
7,174.5
27,150.5
27,115.8
43,155.865
51,57.8
27,103.6
38,136.5
46,100.6
18,107.1
27,163.2
48,107.2
6,93.0
18,194.8
40,143.25
7,103.7
44,89.65
10,104.35
43,143.94
30,134.8
35,99.0
31,126.0
26,121.2
45,107.55
30,92.55
22,140.3
7,116.45
12,132.0
8,102.0
39,109.0
7,204.0
23,128.8
18,101.1
23,91.1
11,90.45
23,84.273
23,108.2
37,102.6
30,122.875
6,96.2
40,77.202
25,114.0
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19,117.555
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35,87.8
11,106.608
18,152.664
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25,168.635
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30,113.278
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49,78.162
22,96.614
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32,124.309
14,115.313
36,117.515
29,148.5
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0,105.0
37,104.279
16,112.429
31,131.205
28,113.543
23,134.885
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19,113.068
30,93.904
31,102.58
26,89.565
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Male
Female
Salary
using DataFrames, Gadfly
set_default_plot_size(14cm, 8cm)
x = range(0.1, stop=4.9, length=30)
D = DataFrame(x=x, y=x.+randn(length(x)))
p = plot(D, x=:x, y=:y, Geom.point,
layer(Stat.smooth(method=:lm, levels=[0.90,0.99]), Geom.line, Geom.ribbon(fill=false)),
Theme(lowlight_color=c->"gray", line_style=[:solid, :dot])
)
x
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4.95.1236639415945415
4.734482758620694.0857910741253916
4.5689655172413794.842611204779619
4.4034482758620695.195002504701055
4.23793103448275853.4375754021151135
4.0724137931034484.107922059526255
3.9068965517241383.992767976624603
3.74137931034482743.1102867960007323
3.5758620689655171.8598553056163665
3.4103448275862073.506423176253704
3.24482758620689673.888036205840213
3.0793103448275863.7097949046331724
2.9137931034482764.751394901784543
2.74827586206896561.8294619728000718
2.58275862068965531.9857062888307766
2.41724137931034472.489270577398729
2.25172413793103441.8653621575453705
2.0862068965517241.8320121533789155
1.92068965517241372.882931073697085
1.75517241379310350.3927434572513049
1.5896551724137931.1669398573016456
1.42413793103448282.1121293554397504
1.25862068965517240.8253280905880598
1.09310344827586210.1632698913543228
0.9275862068965517-0.7137276968158517
0.76206896551724131.7868092494125287
0.5965517241379310.6064197782701288
0.43103448275862066-1.0777001238387638
0.2655172413793103-0.3182382032287451
0.1-0.2225696498949116
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-12
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-6.5
-6.0
-5.5
-5.0
-4.5
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-3.5
-3.0
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-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
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5.0
5.5
6.0
6.5
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-10
0
10
20
-10.0
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
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8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
y
using Gadfly, Random
set_default_plot_size(14cm, 8cm)
Random.seed!(1234)
plot(x=rand(25), y=rand(25), Stat.step, Geom.line)
x
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
-1.0
-0.9
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1.0
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2.0
-1
0
1
2
-1.00
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0.85
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0.95
1.00
1.05
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1.35
1.40
1.45
1.50
1.55
1.60
1.65
1.70
1.75
1.80
1.85
1.90
1.95
2.00
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-1.5
-1.0
-0.5
0.0
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1.0
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2.0
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-1.0
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-0.6
-0.5
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-0.3
-0.2
-0.1
0.0
0.1
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0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
-1
0
1
2
-1.00
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-0.25
-0.20
-0.15
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0.00
0.05
0.10
0.15
0.20
0.25
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0.50
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0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
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1.20
1.25
1.30
1.35
1.40
1.45
1.50
1.55
1.60
1.65
1.70
1.75
1.80
1.85
1.90
1.95
2.00
y
using Gadfly, Distributions, Random
set_default_plot_size(14cm, 8cm)
Random.seed!(1234)
plot(x=rand(1:4, 500), y=rand(500), Stat.x_jitter(range=0.5), Geom.point)
x
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
-5
0
5
10
-5.0
-4.8
-4.6
-4.4
-4.2
-4.0
-3.8
-3.6
-3.4
-3.2
-3.0
-2.8
-2.6
-2.4
-2.2
-2.0
-1.8
-1.6
-1.4
-1.2
-1.0
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-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
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4.0
4.2
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h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-1.5
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0.4
0.5
0.6
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0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
-1
0
1
2
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0.90
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1.15
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1.45
1.50
1.55
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1.90
1.95
2.00
y
using Gadfly, Random
set_default_plot_size(14cm, 8cm)
Random.seed!(1234)
plot(x=rand(10), y=rand(10), Stat.xticks(ticks=[0.0, 0.1, 0.9, 1.0]), Geom.point)
x
0.0
0.1
0.9
1.0
0.94645322623138340.0027764596470082337
0.131025656220859040.7285427707652774
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0.41129411794985050.5525344667850608
0.57986212013413240.749719144596962
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
-1
0
1
2
-1.00
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-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
1.50
1.55
1.60
1.65
1.70
1.75
1.80
1.85
1.90
1.95
2.00
y